SPECIAL TOPICS in OPERATIONS RESEARCH

AIM

The aim of the course is to enable students to learn dynamic programming and to formulate and solve related problems using dynamic programming.

CONTENT

This course contains; Introduction to Optimization,Motivating Examples for Dynamic Programming,Prototypical Example(s) for Dynamic Programming,Structure of Dynamic Programming Problems,Equipment replacement, distribution of effort, and production planning problems,Knapsack, multi-dimensional state, and traveling salesperson problems,Probability Basics,Probabilistic Dynamic Programming-1,Probabilistic Dynamic Programming-2,Dynamic Programming Applications-1,Dynamic Programming Applications-2,Solving Dynamic Programming Examples using Microsoft Excel-1,Solving Dynamic Programming Examples using Microsoft Excel-2 ,Review.

1. 1

   Students model dynamic programming problems.

   Taught by: Discussion Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

2. 2

   Students solve deterministic dynamic programming problems.

   Taught by: Discussion Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

3. 3

   Students solve stochastic dynamic programming problems.

   Taught by: Discussion Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

4. 4

   Students interpret dynamic programming problems.

   Taught by: Discussion Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

WEEKLY PLAN

1. WEEK 1

   Introduction to Optimization

2. WEEK 2

   Motivating Examples for Dynamic Programming

3. WEEK 3

   Prototypical Example(s) for Dynamic Programming

4. WEEK 4

   Structure of Dynamic Programming Problems

5. WEEK 5

   Equipment replacement, distribution of effort, and production planning problems

6. WEEK 6

   Knapsack, multi-dimensional state, and traveling salesperson problems

7. WEEK 7

   Probability Basics

8. WEEK 8

   Probabilistic Dynamic Programming-1

9. WEEK 9

   Probabilistic Dynamic Programming-2

10. WEEK 10

    Dynamic Programming Applications-1

11. WEEK 11

    Dynamic Programming Applications-2

12. WEEK 12

    Solving Dynamic Programming Examples using Microsoft Excel-1

13. WEEK 13

    Solving Dynamic Programming Examples using Microsoft Excel-2

14. WEEK 14

    Review

ASSESSMENT

• Rate of Midterm Exam to Success30%
• Rate of Final Exam to Success70%

WORKLOAD

READING

• Frederik S. Hillier, Gerald J. Lieberman, Introduction to Operations Research, McGraw Hill

TEACHING STAFF

• Assoc.Prof. Yasin GÖÇGÜNCOORDINATOR
• Assoc.Prof. Yasin GÖÇGÜN